class_docstr: Class to imitate gensim's KeyedVectors, but instead getting the vectors from the memory,
  the vectors will be retrieved from a redis db.
  Based on https://engineering.talentpair.com/serving-word-vectors-
  for-distributed-computations-c5065cbaa02f
class_docstr_tok: ["Class","to","imitate","gensim","'s","KeyedVectors",",","but","instead","getting","the","vectors","from","the","memory",",","the","vectors","will","be","retrieved","from","a","redis","db",".","Based","on","https",":","\/\/engineering.talentpair.com\/serving-word-vectors-","for-distributed-computations-c5065cbaa02f"]
docstr: build in method to quickly check whether a word is available in redis
docstr_tok: ["build","in","method","to","quickly","check","whether","a","word","is","available","in","redis"]
code: def __contains__(self, word):
      """
      build in method to quickly check whether a word is available in redis
      """
      return self._redis.hexists(self.key, word)
  
file_path: textpipe/textpipe/wrappers.py
imports: import pickle
  from functools import lru_cache
  from urllib.parse import urlparse
  import numpy
  from gensim.models.keyedvectors import KeyedVectors
  from redis import Redis
  from redis.exceptions import RedisError
  from tqdm import tqdm
label_desc: Use image node_id 4 for calling the RedisKeyedVectors obj's underlying member method code with example usage: obj.__contains__(word) and returns: self